Anomaly detection algorithm can work in modes such as diagnosis and accommodation [1]}. Firstly, the diagnosis method finds the outlier in the data and removes it from the data sample to avoid skewing the distribution. This method is applicable when the distribution of expected behaviors is known. The outliers get excluded when the estimation of the parameters of the distribution [1]}. Secondly, the accommodation method finds the outliers in the data and includes estimating the parameters of the statistical model. The accommodation method is suitable for data streams that account for the effect of concept drift [3]}.
